Clustering is essential to many tasks in pattern recognition and computer
vision. With the advent of deep learning, there is an increasing interest in
learning deep unsupervised representations for clustering analysis. Many works
on this domain rely on variants of auto-encoders and use the encoder outputs as
representations/features for clustering. In this paper, we show that an l2
normalization constraint on these representations during auto-encoder training,
makes the representations more separable and compact in the Euclidean space
after training. This greatly improves the clustering accuracy when k-means
clustering is employed on the representations. We also propose a clustering
based unsupervised anomaly detection method using l2 normalized deep
auto-encoder representations. We show the effect of l2 normalization on anomaly
detection accuracy. We further show that the proposed anomaly detection method
greatly improves accuracy compared to previously proposed deep methods such as
reconstruction error based anomaly detection.

本研究采用 l2 正规化限制自编码器的表示特征，从而提高欧几里德空间中的分离度和紧凑度。同时，提出了一种基于聚类的无监督异常检测方法，并表明其比基于重构误差的异常检测方法具有更高的准确性。

使用 L2 规范化深度自编码表示进行聚类和无监督异常检测

Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep  Auto-Encoder Representations

Sentences are important semantic units of natural language. A generic,
distributional representation of sentences that can capture the latent
semantics is beneficial to multiple downstream applications. We observe a
simple geometry of sentences -- the word representations of a given sentence
(on average 10.23 words in all SemEval datasets with a standard deviation 4.84)
roughly lie in a low-rank subspace (roughly, rank 4). Motivated by this
observation, we represent a sentence by the low-rank subspace spanned by its
word vectors. Such an unsupervised representation is empirically validated via
semantic textual similarity tasks on 19 different datasets, where it
outperforms the sophisticated neural network models, including skip-thought
vectors, by 15% on average.

研究发现句子的词向量表示一般都集中在一个低秩子空间内，通过基于该子空间的无监督表示法在 19 个数据集上测试，平均优于 skip-thought vectors 等神经网络模型 15%。